Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders reveals disturbances in excitation, inhibition, and energy gaps

Abstract

AbstractResting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Biophysical network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). Such approaches miss out on the multi(or cross)-attractor dynamics that have been shown to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multiattractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the highest local excitation (across groups) was observed in the ADHD group, whereas the lowest local inhibition was observed in the bipolar group. In line with these results, the ADHD group had the lowest switching costs (energy gaps) across groups. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders’ pathophysiology.